Illuminating device

ABSTRACT

In an embodiment, an illuminating device includes a light source, a light source control unit, an estimation unit, a first calculating unit, and a second calculating unit. The light source includes light emitters, each having a different spectral distribution. The light source control unit determines an emission intensity of each of the light emitters. The estimation unit estimates a spectral reflectivity of an object. The first calculating unit calculates a first evaluation value, indicating an adequacy of a color of the object visually perceived, based on the spectral distributions and the spectral reflectivity. The second calculating unit calculates a second evaluation value, indicating how much an influence is given by the illuminating light to factors other than a visual sense. The light source control unit determines the emission intensities by which the first evaluation value and the second evaluation value satisfy a restraint condition determined beforehand.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is based upon and claims the benefit of priority fromJapanese Patent Application No. 2011-176983, filed on Aug. 12, 2011; theentire contents of which are incorporated herein by reference.

FIELD

Embodiments of the present invention relate generally to an illuminatingdevice.

BACKGROUND

It has been known that illuminating light is incident on a human'sretina, and then, acts on a region called suprachiasmatic nucleusthrough a retinohypothalamic tract to thereby exert not only visualinfluence but also physiological influence. It has also been known thatthis effect is especially high in light having a lot of short-wavelengthlights near 460 nm. For example, it is pointed that exposure toilluminating light at night inhibits secretion of hormone that ismelatonin, which may affect a sleep quality. Therefore, a reduction ofsuch influence has been demanded for the illumination used at night.

In order to reduce the influence, it is considered that the illuminatinglight does not contain light near 460 nm. However, this undesirablydeteriorates color rendering properties that are significant functionsfor the illumination. The color rendering properties are an indexindicating how close a color appearance of an object under a certainillumination is to a color appearance of the object under a standardlight source. The measurement of the color rendering properties isspecified under Japanese Industrial Standards JIS Z8726.

Evaluation methods of the color rendering properties include a generalcolor rendering index and a special color rendering index. The generalcolor rendering index is obtained by calculating color appearances of 8colors, each having middle level of brightness and vividness, and havinga whole hue. The special color rendering index is obtained bycalculating color appearances of 15 colors, which include, in additionto 8 colors used for the general color rendering index, colors thatreproduce a color and skin color more vivid than those of 8 colors.These values can be calculated by a value of a spectral distribution ofthe illuminating light.

There has been known a technique of reducing a non-visible influence ofillumination with color rendering properties being kept beyond a certainlevel by solving an optimization problem of minimizing a value ofmelatonin secretory inhibiting amount under a restraint condition suchthat the value of the color rendering index is kept beyond a certainlevel.

However, in the related art, even when an object that does not reflectlight near 460 nm is illuminated, for example, light near 460 nm has tobe contained in a certain level in order to keep the value of the colorrendering index beyond a certain level. Specifically, light with awavelength unnecessary for keeping a color appearance of an object iscontained, resulting in that the non-visual influence cannot efficientlybe reduced.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an illuminating device accordingto a first embodiment;

FIG. 2 is a view illustrating an example of an arrangement of lightemitters included in a light source.

FIG. 3 is a block diagram illustrating an estimation unit according tothe first embodiment;

FIG. 4 is a view illustrating an example of a structure of a variablefilter;

FIG. 5 is a flowchart illustrating a control process according to thefirst embodiment;

FIG. 6 is a view illustrating one example of an object illuminated byilluminating light containing plural colors;

FIG. 7 is a block diagram illustrating an illuminating device accordingto a second embodiment;

FIG. 8 is a block diagram illustrating an estimation unit according tothe second embodiment;

FIG. 9 is a block diagram illustrating an illuminating device accordingto a third embodiment;

FIG. 10 is a block diagram illustrating an estimation unit according tothe third embodiment;

FIG. 11 is a view illustrating an example of a configuration of an inputunit; and

FIG. 12 is a view illustrating an example of a modification of an inputunit.

DETAILED DESCRIPTION

In an embodiment, an illuminating device includes a light source, alight source control unit, an estimation unit, a first calculating unit,and a second calculating unit. The light source includes light emitters,each having a different spectral distribution. The light source controlunit determines an emission intensity of each of the light emitters. Theestimation unit estimates a spectral reflectivity of an object to whichilluminating light is irradiated. The first calculating unit calculatesa first evaluation value, indicating an adequacy of a color of theobject visually perceived, based on the spectral distributions and thespectral reflectivity. The second calculating unit calculates a secondevaluation value, indicating how much an influence is given by theilluminating light to factors other than a visual sense, based on thespectral distribution. The light source control unit determines theemission intensities by which the first evaluation value and the secondevaluation value satisfy a restraint condition determined beforehand.

Preferable embodiments of an illuminating device will be described indetail below with reference to the attached drawings.

First Embodiment

The illuminating device according to the first embodiment prevents lightwith a wavelength unnecessary for keeping a color appearance of anobject from being contained in illuminating light, thereby efficientlyreducing non-visual influence.

FIG. 1 is a block diagram illustrating one example of a configuration ofan illuminating device 100 according to the first embodiment. Asillustrated in FIG. 1, the illuminating device 100 includes a lightsource 1, a light source control unit 2, an estimation unit 3, a firstcalculating unit 4, and a second calculating unit 5.

The light source 1 is configured to independently control emissionintensity, and include two or more types of light emitters, each ofwhich has different spectral distribution. The light emitters aretypically LEDs (Light Emitting Diode) corresponding to three primarycolors of RGB.

The LED is compact, and light-weight. Therefore, it is relatively easyto incorporate plural LEDs into one illuminating device, and to controlthe emission intensity of each LED independently.

When the spectral distribution of each LED is defined as P_(i)(λ), andthe emission intensity of each LED is defined as a_(i), the spectralpower distribution P(λ) as the whole illuminating device havingincorporated therein n types of LEDs, each having a different spectraldistribution, can be represented by Equation (1) described below.

$\begin{matrix}{{P(\lambda)} = {\sum\limits_{i = 1}^{n}{a_{i}{P_{i}(\lambda)}}}} & (1)\end{matrix}$

Specifically, the spectral power distribution of the light source 1 canbe considered to be a value of a function determined by n-dimensionalvectors A=(a₁ a₂ a₃ . . . a_(n)).

A value of the color rendering index and a value of melatonin secretoryinhibiting amount can be calculated, if the spectral distribution isfound. Therefore, if a value of n-dimensional vector A is determined,these can be calculated.

The light emitters may include LEDs of three or more colors, or mayinclude white LEDs, each having a different color temperature. The lightemitters may also include both of them. FIG. 2 is a view illustrating anexample of an arrangement of the light emitters included in the lightsource 1. The light source 1 has a configuration as illustrated in FIG.2 in which different types of chip LEDs (light emitters 101 to 103) arearranged. In this case, the light emitted from the light source 1 has amixed color of lights emitted from the respective light emitters. Exceptfor the LED, any other elements can be used as the light emitter, suchas a fluorescent tube, a filament lamp, or sodium lamp. The combinationof these elements may be used as the light emitter. The light source 1may further be provided with a light diffuser plate for mixing colors oflights from plural light emitters.

The light source control unit 2 controls the emission of each lightemitter constituting the light source 1. Typically, the light sourcecontrol unit 2 controls the amount of current flowing through each lightemitter. The light source control unit 2 may control a voltage appliedto each light emitter. A DC current and DC voltage may be controlled, oran AC current and AC voltage may be controlled. Any control method suchas PWM control or phase control may be employed. The light sourcecontrol unit 2 holds therein a table of a value of a spectraldistribution of each light emitter in the light source 1. If the numberof the types of light emitters is n, this table stores values of each ofP_(i)(λ), (i=1, 2, . . . , n) at a predetermined interval within aregion of a visible light. When the emission intensity of each of thelight emitters is defined as a_(i)(i=1, 2, . . . n), the light sourcecontrol unit 2 has a function of calculating the spectral powerdistribution P(λ) of the light source 1 according to Equation (2)described below.

$\begin{matrix}{{P(\lambda)} = {\sum\limits_{i = 1}^{n}\; {a_{i}{P_{i}(\lambda)}}}} & (2)\end{matrix}$

The light source control unit 2 also has a function of reporting thecalculated P(λ) to the first calculating unit 4 and the secondcalculating unit 5. The light source control unit 2 also has a functionof receiving estimated values (first evaluation value and secondevaluation value) calculated by the first calculating unit 4 and thesecond calculating unit 5, and determining a vector A by solving anoptimization problem having the estimated values defined as targetvariables.

The estimation unit 3 estimates a spectral reflectivity of an object(not illustrated) that is to be illuminated by the illuminating lightfrom the light source 1. FIG. 3 is a block diagram illustrating anexample of a configuration of the estimation unit 3 according to thefirst embodiment. The configuration of the estimation unit 3 will bedescribed below. The estimation unit 3 includes an imaging unit 301, avariable filter 302, an imaging control unit 303, and an imageprocessing unit 304.

The imaging unit 301 is an image sensor such as a CCD camera or a CMOScamera. The spectral sensitivity S(λ) of the imaging unit 301 hasalready been known. The imaging unit 301 images an object, which isilluminated by the illuminating light from the light source 1 throughthe variable filter 302, in synchronous with the variable filter 302 inaccordance with the report from the imaging control unit 303. Thecaptured image is transmitted to the image processing unit 304.

The variable filter 302 can change plural filters whose spectraltransmittance has been known. The variable filter 302 changes thespectral transmittance in accordance with the report from the imagingcontrol unit 303. FIG. 4 is a view illustrating an example of astructure of the variable filter 302. The variable filter 302 isconfigured such that plural filters, which are physically different,rotate and move so as to change the spectral transmittance of thevariable filter 302 as illustrated in FIG. 4. The variable filter 302may be a liquid crystal tunable filter that can electrically control thespectral transmittance. Supposing that m types of spectraltransmittances can be realized, each spectral transmittance is definedas T_(j)(λ), (j=1, 2, . . . , m).

The imaging control unit 303 controls the variable filter 302 and theimaging unit 301 such that the variable filter 302 changes the spectraltransmittance, and then, the imaging unit 301 captures an image.

The image processing unit 304 estimates the spectral reflectivity of theobject, which is illuminated by the illuminating light from the lightsource 1, from plural images captured by the imaging unit 301 underdifferent spectral transmittances of the variable filter 302. The methodof estimating the spectral reflectivity by the image processing unit 304is as described below.

The spectral reflectivity on any portion of the object is defined asR(λ), the spectral power distribution of the light source 1 is definedas P(λ), the spectral sensitivity of the imaging unit 301 is defined asS(λ), and the spectral transmittance that can be changed by the variablefilter 302 is defined as T_(j)(λ), (j=1, 2, . . . , m). The values ofS(λ) and T_(j)(λ) are held as a table (not illustrated) in the imageprocessing unit 304, for example.

The output value V_(j) of the imaging unit 301 to the object, capturedthrough the variable filter 302 whose spectral transmittance is changedto the jth spectral transmittance, is represented by Equation (3)described below.

V _(j)=∫_(λ) ₁ ^(λ) ^(w) P(λ)R(λ)T _(j)(λ)S(λ)dλ  (3)

λ₁ and λ_(w) respectively indicate a lower limit and an upper limit of awavelength where the sensitivity of the imaging unit 301 is guaranteed.When the integration described above is approximated by a discretevalue, and resolved, a determinant illustrated by Equation (4) isobtained. Here, Δλ is a quantization range for performing thediscretization.

$\begin{matrix}{\begin{bmatrix}V_{1} \\V_{2} \\\vdots \\V_{m}\end{bmatrix} = {\begin{bmatrix}{{P\left( \lambda_{1} \right)}{T_{1}\left( \lambda_{1} \right)}{S\left( \lambda_{1} \right)}{\Delta\lambda}} & {{P\left( \lambda_{2} \right)}{T_{1}\left( \lambda_{2} \right)}{S\left( \lambda_{2} \right)}{\Delta\lambda}} & \cdots & {{P\left( \lambda_{w} \right)}{T_{1}\left( \lambda_{w} \right)}{S\left( \lambda_{w} \right)}{\Delta\lambda}} \\{{P\left( \lambda_{1} \right)}{T_{2}\left( \lambda_{1} \right)}{S\left( \lambda_{1} \right)}{\Delta\lambda}} & {{P\left( \lambda_{2} \right)}{T_{2}\left( \lambda_{2} \right)}{S\left( \lambda_{2} \right)}{\Delta\lambda}} & \cdots & {{P\left( \lambda_{w} \right)}{T_{2}\left( \lambda_{w} \right)}{S\left( \lambda_{w} \right)}{\Delta\lambda}} \\\vdots & \vdots & \vdots & \vdots \\{{P\left( \lambda_{1} \right)}{T_{m}\left( \lambda_{1} \right)}{S\left( \lambda_{1} \right)}{\Delta\lambda}} & {{P\left( \lambda_{2} \right)}{T_{m}\left( \lambda_{2} \right)}{S\left( \lambda_{2} \right)}{\Delta\lambda}} & \cdots & {{P\left( \lambda_{w} \right)}{T_{m}\left( \lambda_{w} \right)}{S\left( \lambda_{w} \right)}{\Delta\lambda}}\end{bmatrix}\begin{bmatrix}{R\left( \lambda_{1} \right)} \\{R\left( \lambda_{2} \right)} \\\vdots \\{R\left( \lambda_{w} \right)}\end{bmatrix}}} & (4)\end{matrix}$

When the matrix at the left in the right side in Equation (4) is put asF, and a pseudo inverse matrix G of F is obtained by wiener method, thespectral reflectivity R(λ) of the object can be obtained by Equation (5)described below.

$\begin{matrix}{\begin{bmatrix}{R\left( \lambda_{1} \right)} \\{R\left( \lambda_{2} \right)} \\\vdots \\{R\left( \lambda_{w} \right)}\end{bmatrix} = {G\begin{bmatrix}V_{1} \\V_{2} \\\vdots \\V_{m}\end{bmatrix}}} & (5)\end{matrix}$

In the above description, the number of channels of the imaging unit 301is 1 (the monochromatic image). If the imaging unit 301 having 3channels of REB is used, for example, the number of the equations inEquation (4) can be tripled. Therefore, the spectral reflectivity can beestimated with high precision.

The method of estimating the spectral reflectivity of the object is notlimited thereto. Any other techniques may be used.

The second calculating unit 5 estimates a non-visual influence quantityY1 of the illumination based on the spectral power distribution P(λ) ofthe light source 1 reported from the light source control unit 2. Theestimated non-visual influence quantity Y1 is reported to the lightsource control unit 2.

The non-visual influence quantity Y1 (second evaluation value) indicateshow much the influence is given by the illuminating light to non-visualfactors. For example, the non-visual influence quantity Y1 is a value ofintegral of a product of a action spectrum for melatonin suppression andthe spectral power distribution P(λ) of the light source 1. The value ofthe melatonin secretory inhibition prediction expression considering aresponse of a cone, a rod, and a melanopsin-containing ganglion cellinto may be employed as the non-visual influence quantity Y1.

The value of integral of the product of the melatonin secretoryinhibiting action spectral and the spectral power distribution of thelight source 1 is defined by Equation (6) described below wherein thespectral power distribution of the light source 1 is defined as P(λ),and the action spectrum for melatonin suppression is defined as M₁(λ).

Y ₁=∫_(380 nm) ^(730 nm) P(λ)M ₁(λ)dλ  (6)

The value of the melatonin secretory inhibition prediction expressionconsidering the response of a cone, a rod, and a melanopsin-containingganglion cell is classified according to a value of T (Equation (7)).When T≧0, it can be calculated by Equation (8), and when T<0, it can becalculated by Equation (9).

$\begin{matrix}{T = {{\int_{380\mspace{14mu} {nm}}^{730\mspace{14mu} {nm}}{{P(\lambda)}{S(\lambda)}\ {\lambda}}} - {k{\int_{380\mspace{14mu} {nm}}^{730\mspace{14mu} {nm}}{{P(\lambda)}{V_{10}(\lambda)}\ {\lambda}}}}}} & (7) \\{Y_{1} = {\left\lbrack {\left( {{\alpha_{1}{\int_{380\mspace{14mu} {nm}}^{730\mspace{14mu} {nm}}{{P(\lambda)}{M_{2}(\lambda \ )}{\lambda}}}} - b_{1}} \right) + {\alpha_{2}\left( {{\int_{380\mspace{14mu} {nm}}^{780\mspace{14mu} {nm}}{{P(\lambda)}{S(\lambda)}\ {\lambda}}} - {k{\int_{380\mspace{14mu} {nm}}^{730\mspace{14mu} {nm}}{{P(\lambda)}{V_{10}(\lambda)}\ {\lambda}}}}} \right)} - b_{2}} \right\rbrack - {\alpha_{3}\left\lbrack {1 - {\exp \left( {- \frac{\int_{380\mspace{14mu} {nm}}^{730\mspace{14mu} {nm}}{{P(\lambda)}{V^{\prime}(\lambda)}\ {\lambda}}}{rodSat}} \right)}} \right\rbrack}}} & (8) \\{Y_{1} = {\alpha_{1}{\int_{380\mspace{14mu} {nm}}^{730\mspace{14mu} {nm}}{{P(\lambda)}{M_{2}\left( {{\lambda \ {\lambda}} - b_{1}} \right.}}}}} & (9)\end{matrix}$

Here, constants are set as k=0.31, α₁=0.285, α₂=0.2, α₃=0.72, b1=0.01,b2=0.001, and rodsat=6.5. M₂(λ) is the spectral reaction sensitivity ofthe melanopsin-containing ganglion cell, V₁₀(λ) is the spectral reactionsensitivity of an L cone and M cone, V′(λ) is the spectral reactionsensitivity of the rod, and S(λ) is the spectral sensitivity of an Scone.

The first calculating unit 4 calculates the output value (firstevaluation value) indicating an adequacy of a color of an object (colorappearance of an object) visually perceived, based on the spectraldistribution of the light source 1 and the spectral reflectivity of theobject. For example, the first calculating unit 4 estimates the colorappearance of the object under a standard light source based on thevalue R(λ), estimated by the estimation unit 3, of the spectralreflectivity of the object illuminated by the illuminating light fromthe light source 1, and the value P(λ) of the spectral powerdistribution of the light source 1 determined by the light sourcecontrol unit 2. The specific method of the estimation will be describedbelow.

Firstly, the first calculating unit 4 obtains a correlated colortemperature of emission color by the spectral power distribution P(λ) ofthe light source 1, and determines the light source used as the standardlight source. When the correlated color temperature of P(λ) is less than5000 K, the first calculating unit 4 defines light with the correlatedcolor temperature equal to P(λ) of a complete radiator as the standardlight source. When it is 5000 K or more, the first calculating unit 4defines light with the correlated color temperature equal to P(λ) of CIEdaylight as the standard light source. The value of the spectraldistribution of the standard light source obtained here is defined asS(λ) below. This value is stored as a table (not illustrated) in thefirst calculating unit 4, for example.

A coordinate value (X_(p), Y_(p), Z_(p)) corresponding to the lightsource 1 in an XYZ color system and a coordinate value (X_(s), Y_(s),Z_(s)) corresponding to the standard light source are obtained byEquation (10) to Equation (17) described below.

X _(p) =K _(p)∫_(380 nm) ^(780 nm) P(λ){dot over (x)}(λ)dλ  (10)

Y _(p) =K _(p)∫_(380 nm) ^(780 nm) P(λ){dot over (y)}(λ)dλ  (11)

Z _(p) =K _(p)∫_(380 nm) ^(780 nm) P(λ)ż(λ)dλ  (12)

wherein

$\begin{matrix}{K_{p} = \frac{100}{\int_{380\mspace{14mu} {nm}}^{780\mspace{14mu} {nm}}{{P(\lambda)}{\overset{.}{y}(\lambda \ )}{\lambda}}}} & (13) \\{X_{s} = {K_{s}{\int_{380\mspace{14mu} {nm}}^{780\mspace{14mu} {nm}}{{S(\lambda)}{\overset{.}{x}(\lambda)}\ {\lambda}}}}} & (14) \\{Y_{s} = {K_{s}{\int_{380\mspace{14mu} {nm}}^{780\mspace{14mu} {nm}}{{S(\lambda)}{\overset{.}{y}(\lambda)}\ {\lambda}}}}} & (15) \\{Z_{s} = {K_{s}{\int_{380\mspace{14mu} {nm}}^{780\mspace{14mu} {nm}}{{S(\lambda)}{\overset{.}{z}(\lambda \ )}{\lambda}}}}} & (16)\end{matrix}$

wherein

$\begin{matrix}{K_{s} = \frac{100}{\int_{380\mspace{14mu} {nm}}^{780\mspace{14mu} {nm}}{{S(\lambda)}{\overset{.}{y}(\lambda)}\ {\lambda}}}} & (17)\end{matrix}$

wherein {dot over (x)}(λ), {dot over (y)}(λ), ż(λ) are color-matchingfunctions in an XYZ color system.

Next, a coordinate value (u_(p), v_(p)) corresponding to the lightsource 1 on a CIE1960UCS chromaticity diagram and a coordinate value(u_(s), v_(s)) corresponding to the standard light source are obtainedby Equation (18) to Equation (21) described below.

$\begin{matrix}{u_{s} = \frac{4X_{s}}{X_{s} + {15Y_{s}} + {3Z_{s}}}} & (18) \\{v_{s} = \frac{6Y_{s}}{X_{s} + {15Y_{s}} + {3Z_{s}}}} & (19) \\{u_{p} = \frac{4X_{p}}{X_{p} + {15Y_{p}} + {3Z_{p}}}} & (20) \\{v_{p} = \frac{6Y_{p}}{X_{p} + {15Y_{p}} + {3Z_{p}}}} & (21)\end{matrix}$

A value (X_(pr), Y_(pr), Z_(pr))of a tristimulus value of an objectcolor under the light source 1 and a value (X_(sr), Y_(sr), Z_(sr))thereof under the standard light source are obtained by Equation (22) toEquation (29) described below.

X _(sr) =K _(p)∫_(380 nm) ^(780 nm) S(λ)R(λ){dot over (x)}(λ)dλ  (22)

Y _(sr) =K _(p)∫_(380 nm) ^(780 nm) S(λ)R(λ){dot over (y)}(λ)dλ  (23)

Z _(sr) =K _(p)∫_(380 nm) ^(780 nm) S(λ)R(λ)ż(λ)dλ  (24)

wherein

$\begin{matrix}{K_{sr} = \frac{100}{\int_{380\mspace{14mu} {nm}}^{780\mspace{14mu} {nm}}{{S(\lambda)}{R(\lambda)}{\overset{.}{y}(\lambda)}\ {\lambda}}}} & (25) \\{X_{pr} = {K_{p}{\int_{380\mspace{14mu} {nm}}^{780\mspace{14mu} {nm}}{{P(\lambda)}{R(\lambda)}{\overset{.}{x}(\lambda)}\ {\lambda}}}}} & (26) \\{Y_{pr} = {K_{p}{\int_{380\mspace{14mu} {nm}}^{780\mspace{14mu} {nm}}{{P(\lambda)}{R(\lambda)}{\overset{.}{y}(\lambda)}\ {\lambda}}}}} & (27) \\{Z_{pr} = {K_{p}{\int_{380\mspace{14mu} {nm}}^{780\mspace{14mu} {nm}}{{P(\lambda)}{R(\lambda)}{\overset{.}{z}(\lambda)}\ {\lambda}}}}} & (28)\end{matrix}$

wherein

$\begin{matrix}{K_{pr} = \frac{100}{\int_{380\mspace{14mu} {nm}}^{780\mspace{14mu} {nm}}{{P(\lambda)}{R(\lambda)}{\overset{.}{y}(\lambda)}\ {\lambda}}}} & (29)\end{matrix}$

From these values, a coordinate value (u_(pr), v_(pr)) under the lightsource 1 on the CIE1960UCS chromaticity diagram and a coordinate value(u_(sr), v_(sr)) under the standard light source are obtained byEquation (30) to Equation (33) described below.

$\begin{matrix}{u_{sr} = \frac{4X_{sr}}{X_{sr} + {15Y_{sr}} + {3Z_{sr}}}} & (30) \\{v_{sr} = \frac{6Y_{sr}}{X_{sr} + {15Y_{sr}} + {3Z_{sr}}}} & (31) \\{u_{pr} = \frac{4X_{pr}}{X_{pr} + {15Y_{pr}} + {3Z_{pr}}}} & (32) \\{v_{pr} = \frac{6Y_{pr}}{X_{pr} + {15Y_{pr}} + {3Z_{pr}}}} & (33)\end{matrix}$

Next, a chromatic-adaptation transform is executed in accordance withEquation (34) to Equation (37) described below.

$\begin{matrix}{u_{p}^{\prime} = u_{s}} & (34) \\{v_{p}^{\prime} = v_{s}} & (35) \\{u_{pr}^{\prime} = \frac{10.872 + {0.404\frac{c_{s}}{c_{p}}c_{pr}} - {4\frac{d_{s}}{d_{p}}d_{pr}}}{16.518 + {1.481\frac{c_{s}}{c_{p}}c_{pr}} - {4\frac{d_{s}}{d_{p}}d_{pr}}}} & (36) \\{v_{pr}^{\prime} = \frac{5.520}{16.518 + {1.481\frac{c_{s}}{c_{p}}c_{pr}} - {4\frac{d_{s}}{d_{p}}d_{pr}}}} & (37)\end{matrix}$

Here, c_(s), c_(p), c_(pr), d_(s), d_(p), and d_(pr) are obtained byEquation (38) to Equation (43) described below.

$\begin{matrix}{c_{s} = {\frac{1}{v_{s}}\left( {4.0 - u_{s} - {10.0v_{s}}} \right)}} & (38) \\{c_{p} = {\frac{1}{v_{p}}\left( {4.0 - u_{p} - {10.0v_{p}}} \right)}} & (39) \\{c_{pr} = {\frac{1}{v_{pr}}\left( {4.0 - u_{pr} - {10.0v_{pr}}} \right)}} & (40) \\{v_{s} = {\frac{1}{v_{s}}\left( {{1.708v_{s}} + 0.404 - {1.481u_{s}}} \right)}} & (41) \\{v_{p} = {\frac{1}{v_{p}}\left( {{1.708v_{p}} + 0.404 - {1.481u_{p}}} \right)}} & (42) \\{v_{pr} = {\frac{1}{v_{pr}}\left( {{1.708v_{pr}} + 0.404 - {1.481u_{pr}}} \right)}} & (43)\end{matrix}$

A coordinate (W*_(sr), U*_(sr), V*_(sr)) under the standard light sourcein a CIE1964 uniform color space and a coordinate (W*_(pr), U*_(pr),V*_(pr)) under the light source 1 are obtained by Equation (44) toEquation (49) described below.

W* _(sr)=25(Y _(sr))^(1/3)−17   (44)

U* _(sr)=13W* _(sr)(u _(sr) −u _(s))   (45)

V* _(sr)=13W* _(sr)(v _(sr) −u _(s))   (46)

W* _(pr)=25(Y _(pr))^(1/3)−17   (47)

U* _(pr)=13W* _(pr)(u′ _(pr) −u′ _(p))   (48)

V* _(pr)=13W* _(pr)(v′ _(pr) −v′ _(p))   (49)

A chromaticity difference ΔE is obtained by Equation (50) describedbelow through the procedure described above.

ΔE=√{square root over ((W* _(sr) −W* _(pr))²+(U* _(sr) −U* _(pr))²+(V*_(sr) −V* _(pr))²)}{square root over ((W* _(sr) −W* _(pr))²+(U* _(sr)−U* _(pr))²+(V* _(sr) −V* _(pr))²)}{square root over ((W* _(sr) −W*_(pr))²+(U* _(sr) −U* _(pr))²+(V* _(sr) −V* _(pr))²)}  (50)

The value of ΔE is obtained for each pixel of the image captured by theimaging unit 301. Therefore, the value of each pixel is defined asΔE_(hw)(h=1, 2, . . . , H) (w=1, 2, . . . , W), and the total of thechromaticity difference of the whole image is specified as farness incolor appearance under two illuminations.

$\begin{matrix}{{\Delta \; E_{sum}} = {\sum\limits_{h = 1}^{H}\; {\sum\limits_{w = 1}^{W}\; {\Delta \; E_{hw}}}}} & (51)\end{matrix}$

In Equation (51), the values are summed up over the whole region of theimage. However, only the values in a specific region may be consideredinto.

A closeness in the color appearance can be defined by any functionY2=F(ΔE_(sum)), wherein Y2 becomes smaller as the value of ΔE_(sum)increases. The value Y2 of the closeness in the color appearance becomesthe output value (first evaluation value) of the first calculating unit4.

The method described above is a method of calculating the colorappearance under the standard light source in the CIE1964 uniform colorspace and the closeness in the color appearance under the light source 1of the illuminating device 100. This method can be replaced by a methodof obtaining a chromaticity difference in another color space such asL*a*b* color space. Alternatively, an equation of chromaticitydifference such as CIEDE2000 can be used instead.

The control process of the illuminating device 100 thus configuredaccording to the first embodiment will be described next with referenceto FIG. 5. FIG. 5 is a flowchart illustrating the whole control processaccording to the first embodiment.

The estimation unit 3 estimates the spectral reflectivity of an objectilluminated by the illumination (step S101), The result of theestimation is reported to the first calculating unit 4.

The light source control unit 2 optimizes the vector A (A=a₁ a₂ a₃ . . .a_(n)), which is a value determining the emission intensity of the lightsource 1, based on the optimization condition determined beforehand(step S102). In this case, the light source control unit 2 reports avalue of P(λ) corresponding to a temporary vector A to the secondcalculating unit 5 and the first calculating unit 4. The secondcalculating unit 5 and the first calculating unit 4 return the estimatedvalues Y1 and Y2 corresponding to the value of the temporary vector A.The condition for optimization used here is the one for minimizing Y1with Y2 being kept to be not less than a certain value. This ismathematically a general optimization problem with restraint condition.The value of the vector A satisfying the above-mentioned condition canbe obtained by using a general optimization method such as a gradientmethod or simulated annealing.

The light source control unit 2 controls such that each of the lightemitters of the light source 1 emits light based on the determined valueof the vector A (step S103).

With this process, when an object that hardly reflects blue light(especially, light with a wavelength near 460 nm) is illuminated, forexample, the light source 1 does not have to contain blue light even ifthe value of Y2 is likely to be kept large. On the other hand, if thevalue of the color rendering index is likely to be kept large as in therelated case, the light source 1 has to contain blue light. Therefore,the present embodiment that tries to keep the value of Y2 large canreduce the value of Y1 more than the related method that tries to keepthe value of the color rendering index. Specifically, the presentembodiment can efficiently reduce the non-visual influence due to theillumination without deteriorating the color appearance of an objectilluminated by the illumination.

The restraint condition used in step S102 is not limited to thecondition in which the Y1 is minimized with the Y2 being kept to be notless than a certain value. For example, the condition such that the Y2is maximized with the Y1 being kept to be not more than a certain valuemay be employed, if the reduction in the non-visual influence takespriority. If an arousal level is to be enhanced, the condition such thatthe Y1 is maximized with the Y2 being kept to be not less than a certainvalue may be employed.

Modification of First Embodiment

FIG. 6 is a view illustrating one example of an object illuminated byilluminating light containing plural colors. As illustrated in FIG. 6,characters 602 having a spectral reflectivity of R_(B)(λ) are written ona background 601 having a spectral reflectivity of R_(A)(λ). If thechromaticity difference of the colors perceived in the region of thebackground 601 and the region of the characters 602 is sufficientlygreat, the characters 602 can be read even if the colors are greatlyshifted from the colors perceived under the standard light source.

Specifically, the value of Y2 used for obtaining the vector A by solvingthe above-mentioned optimization problem may be replaced by achromaticity difference in plural regions, each having differentspectral reflectivity. For this replacement, what is done in the firstcalculating unit 4 is replaced as described below in the presentmodification.

Firstly, a cluster analysis is performed to the value R(λ) of thespectral reflectivity of each pixel estimated by the estimation unit 3.With this process, the value R(λ) of the spectral reflectivity isclassified into a cluster belonging to each region. The values of thespectral reflectivity corresponding to the representative values (e.g.,centroids) of the first region and the second region are set as R₁(λ)and R₂(λ).

The coordinate in the CIE1964 uniform color space under the light source1 can be calculated in the same manner as in the above-mentioned method.The coordinate of the first region is set as (W*_(pr1), U*_(pr1),V*_(pr1)), and the coordinate of the second region is set as (W*_(pr2),U*_(pr2), V*_(pr2)).

The chromaticity difference ΔE12 between the first region and the secondregion can be obtained by Equation (52) described below. This value canbe set as the output value Y2 of the first calculating unit 4.

ΔE ₁₂=√{square root over ((W* _(pr1) −W* _(pr2))²+(U* _(pr1) −U*_(pr2))²+(V* _(pr1) −V* _(pr2))²)}{square root over ((W* _(pr1) −W*_(pr2))²+(U* _(pr1) −U* _(pr2))²+(V* _(pr1) −V* _(pr2))²)}{square rootover ((W* _(pr1) −W* _(pr2))²+(U* _(pr1) −U* _(pr2))²+(V* _(pr1) −V*_(pr2))²)}  (52)

In this modification, the method of obtaining the chromaticitydifference in the CIE1964 uniform color space can be replaced by amethod of obtaining a chromaticity difference in another color spacesuch as L*a*b* color space. Alternatively, an equation of chromaticitydifference such as CIEDE2000 can be used instead.

The above-mentioned process is for the case in which there are tworegions, each having a different spectral reflectivity. However, thesimilar process can be applied to the case in which there are three ormore regions. In this case, the chromaticity difference may becalculated for each combination of the respective regions, and itsaverage or minimum value may be set as the output value of the firstcalculating unit 4.

For example, when characters (reflecting generally wavelengths of red,green and blue) that look white under the white light source are writtenon a background (that hardly reflects light of all wavelengths) thatlooks black under the white light source, the characters look yellow,which is very far from black, even if the light source 1 does notcontain blue light, because the character region reflects light havingwavelengths of red and green. Therefore, the value of Y2 can be keptlarge without the inclusion of the blue light that gives a greatnon-visual influence. Specifically, the present modification can moreefficiently reduce the non-visual influence.

The case between the background color and the character color isdescribed above. However, the modification is applicable for a caseexcept for the case between the background and characters, e.g., for acase in which different colors can be distinguished (e.g., check fordefective goods).

Second Embodiment

FIG. 7 is a block diagram illustrating an example of a configuration ofan illuminating device 100-2 according to a second embodiment. Asillustrated in FIG. 7, the illuminating device 100-2 includes a lightsource 1, a light source control unit 2-2, an estimation unit 3-2, afirst calculating unit 4, and a second calculating unit 5.

In the second embodiment, the functions of the light source control unit2-2 and the estimation unit 3-2 are different from those in the firstembodiment. The other components and functions are similar to those inFIG. 1, which is the block diagram of the illuminating device 100according to the first embodiment. Therefore, the same components areidentified by the same numerals, and redundant description thereof willnot be repeated here.

Instead of the function of the light source control unit 2 in the firstembodiment, the light source control unit 2-2 has a function ofcontrolling the spectral distribution of the light source 1 inaccordance with the input from the estimation unit 3-2.

The estimation unit 3-2 has a function of estimating the spectralreflectivity of an object illuminated by the illuminating light from thelight source 1, and also has a function of giving an instruction to thelight source control unit 2-2 to change the spectral distribution. FIG.8 is a block diagram illustrating the estimation unit 3-2 according tothe second embodiment. The estimation unit 3-2 includes an imaging unit301, an imaging control unit 303-2, and an image processing unit 304-2.The estimation unit 3-2 in the second embodiment is different from thatin the first embodiment in that it does not include the variable filter302.

The imaging control unit 303-2 issues a request of changing the spectraldistribution of the light source 1 and a request of sequentiallyexecuting an image-capture by the imaging unit 301 to the light sourcecontrol unit 2-2. The light source control unit 2-2 controls the lightsource 1 to have different m types of spectral distributions accordingto the request.

The image processing unit 304-2 estimates the spectral reflectivity ofthe object, which is illuminated by the illuminating light from thelight source 1, from plural images captured by the imaging unit 301under different spectral distributions of the light source 1. The methodof estimating the spectral reflectivity by the image processing unit304-2 according to the present embodiment is as described below.

The spectral reflectivity on any portion of the object is defined asR(λ), the m types of spectral distributions of the light source 1 aredefined as P_(j)(λ), (j=1, 2, . . . , m), and the spectral sensitivityof the imaging unit 301 is defined as S(λ).

The output value V_(j) of the imaging unit 301 to the object capturedunder the light source 1 having the j-th type of spectral distributionis represented by Equation (53) described below.

V _(j)=∫_(λ) ₁ ^(λ) ^(w) P _(j)(λ)R(λ)S(λ)dλ  (53)

When the integration described above is approximated by a discretevalue, and resolved, a determinant illustrated by Equation (54) isobtained.

$\begin{matrix}{\begin{bmatrix}V_{1} \\V_{2} \\\vdots \\V_{m}\end{bmatrix} = {\begin{bmatrix}{{P_{1}\left( \lambda_{1} \right)}{S\left( \lambda_{1} \right)}{\Delta\lambda}} & {{P_{1}\left( \lambda_{2} \right)}{S\left( \lambda_{2} \right)}{\Delta\lambda}} & \cdots & {{P_{1}\left( \lambda_{w} \right)}{S\left( \lambda_{w} \right)}{\Delta\lambda}} \\{{P_{2}\left( \lambda_{1} \right)}{S\left( \lambda_{1} \right)}{\Delta\lambda}} & {{P_{2}\left( \lambda_{2} \right)}{S\left( \lambda_{2} \right)}{\Delta\lambda}} & \cdots & {{P_{2}\left( \lambda_{w} \right)}{S\left( \lambda_{w} \right)}{\Delta\lambda}} \\\vdots & \vdots & \vdots & \vdots \\{{P_{m}\left( \lambda_{1} \right)}{S\left( \lambda_{1} \right)}{\Delta\lambda}} & {{P_{m}\left( \lambda_{2} \right)}{S\left( \lambda_{2} \right)}{\Delta\lambda}} & \cdots & {{P_{m}\left( \lambda_{w} \right)}{S\left( \lambda_{w} \right)}{\Delta\lambda}}\end{bmatrix}\begin{bmatrix}{R\left( \lambda_{1} \right)} \\{R\left( \lambda_{2} \right)} \\\vdots \\{R\left( \lambda_{w} \right)}\end{bmatrix}}} & (54)\end{matrix}$

When the matrix at the left in the right side in Equation (54) is put asF, and a pseudo inverse matrix G of F is obtained by wiener method, thespectral reflectivity R(λ) of the object can be obtained by Equation(55) described below.

$\begin{matrix}{\begin{bmatrix}{R\left( \lambda_{1} \right)} \\{R\left( \lambda_{2} \right)} \\\vdots \\{R\left( \lambda_{w} \right)}\end{bmatrix} = {G\begin{bmatrix}V_{1} \\V_{2} \\\vdots \\V_{m}\end{bmatrix}}} & (55)\end{matrix}$

As in the first embodiment, if the imaging unit 301 having 3 channels ofRGB is used, for example, the number of the equations in Equation (54)can be tripled. Therefore, the spectral reflectivity can be estimatedwith high precision.

As described above, even the illuminating device according to the secondembodiment that does not include the variable filter can provide thesame effect as that of the first embodiment. The variable filter canfurther be provided in the second embodiment, as in the firstembodiment. In this case, when the types of the variable filter aredefined as m₁, and the types of the different spectral distributions bythe light source 1 are defined as m₂, the number of the equations inEquation (54) can be a maximum of m₁×m₂. Accordingly, the spectralreflectivity of the object can more precisely be estimated.

Third Embodiment

In the third embodiment, spectral reflectivity of an object illuminatedby an illuminating device is estimated based on a user's manual input.FIG. 9 is a block diagram illustrating an example of a configuration ofan illuminating device 100-3 according to the third embodiment. Asillustrated in FIG. 9, the illuminating device 100-3 includes a lightsource 1, a light source control unit 2, an estimation unit 3-3, a firstcalculating unit 4, and a second calculating unit 5.

FIG. 10 is a block diagram illustrating an example of a configuration ofthe estimation unit 3-3 according to the third embodiment. Theestimation unit 3-3 includes an input unit 305, an input processing unit306, and a spectral reflectivity database 307.

The input unit 305 is composed of a touch panel display, for example.FIG. 11 is a view illustrating an example of a configuration of theinput unit 305. As illustrated in FIG. 11, the user uses the input unit305 to select a color used for an object (e.g., a book) illuminated bythe illumination. The information selected in the input unit 305 isreported to the input processing unit 306. The input unit 305 may becomposed of a physical button or the like, instead of the touch paneldisplay.

The input processing unit 306 estimates a spectral reflectivity withreference to the spectral reflectivity database 307 based on theinformation reported from the input unit 305. The result of theestimation is reported to the first calculating unit 4.

The spectral reflectivity database 307 is a storage unit that storesspectral reflectivity of a typical (average) sheet or ink for eachcolor, for example. The spectral reflectivity database 307 can becomposed of a storage medium popularly used, such as HDD (Hard DiskDrive), an optical disk, a memory card, and a RAM (Random AccessMemory).

The spectral reflectivity database 307 transmits the value of thespectral reflectivity P(λ) corresponding to each color to the inputprocessing unit 306 in accordance with the inquiry from the inputprocessing unit 306. The spectral reflectivity database 307 may bepresent on external network such as Web. In this case, the inputprocessing unit 306 has a function of connecting to the network.

With this configuration, the estimation unit 3-3 in the third embodimentcan estimate the spectral reflectivity of the object. The thirdembodiment can realize functions like those of the first and secondembodiments without mounting a high-cost camera (imaging unit) orvariable filter to the estimation unit 3-3.

Modification of Third Embodiment

In the present modification, a user does not directly designate a colorof an object illuminated by the illumination, but designates a name ofan object illuminated by the illumination.

FIG. 12 is a view illustrating an example of a configuration of theinput unit 305 in the present modification. In the present modification,the user can select a proper name or popular name of an objectilluminated by the illumination, as illustrated in FIG. 12. A mannerother than that of designating a specific publication as illustrated inFIG. 12 may be employed. For example, it can be configured such that theobject is designated in a rough classification such as “newspaper”,“weekly pictorial magazine”, or “paperback book”. The object that can beselected is not limited to the above-mentioned publications.

The spectral reflectivity database 307 stores spectral reflectivity forthe sheet and each color of ink used for the object designated by theinput unit 305. The spectral reflectivity database 307 transmits thespectral reflectivity P(λ) corresponding to each of the used colors tothe input processing unit 306 in accordance with the inquiry from theinput processing unit 306. The spectral reflectivity database 307 may beprovided or may be present on external network such as Web. The spectralreflectivity database 307 can be configured such that, when a newmagazine is first published, or when a used sheet or a type of used inkis changed, the content thereof can be updated on a case-by-case basis.

The present modification can spare the user the trouble of designatingthe color of the object illuminated by the illumination. Since thespectral reflectivity of the actual object can be used, the spectraldistribution of the light source can be optimized based on thehighly-precise spectral reflectivity.

As described above, the first to the third embodiments can evaluate acolor appearance of an object, which is illuminated by illuminatinglight from the light source, in consideration of the spectralreflectivity of the object, in order to prevent light with a wavelengthunnecessary for keeping the color appearance of the object from beingcontained in the illuminating light. Consequently, the non-visualinfluence can more efficiently be reduced.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the inventions. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of theinventions. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the inventions.

1. An illuminating device comprising: a light source configured to include plural light emitters, each having a different spectral distribution; a light source control unit configured to determine an emission intensity of each of the light emitters, and control the light emitters to have the emission intensities; an estimation unit configured to estimate a spectral reflectivity of an object to which illuminating light is irradiated from the light source; a first calculating unit configured to calculate a first evaluation value, indicating an adequacy of a color of the object visually perceived, based on the spectral distributions and the spectral reflectivity; and a second calculating unit configured to calculate a second evaluation value, indicating how much an influence is given by the illuminating light to factors other than a visual sense, based on the spectral distributions, wherein the light source control unit determines the emission intensities by which the first evaluation value and the second evaluation value satisfy a restraint condition determined beforehand.
 2. The device according to claim 1, wherein the first evaluation value is a magnitude in a difference in appearances among plural regions included in the object.
 3. The device according to claim 1, wherein the restraint condition is a condition in which the second evaluation value decreases with the first evaluation value being kept to be not less than a predetermined fixed value, or a condition in which the second evaluation value increases with the first evaluation value being kept to be not less than the fixed value.
 4. The device according to claim 1, wherein the second evaluation value is an integral of a product of the spectral distributions and a action spectrum for melatonin suppression, or a value of a melatonin secretory inhibition prediction expression based on the responses of a cone, a rod, and a melanopsin-containing ganglion cell.
 5. The device according to claim 1, wherein the estimation unit includes: an imaging unit configured to capture an image of the object; and an image processing unit configured to estimate the spectral reflectivity based on the image and the spectral distributions.
 6. The device according to claim 5, wherein the imaging unit captures an image of the object through a variable filter of which spectral transmittance is changeable, and the image processing unit estimates the spectral reflectivity based on the plural images, which are captured through the variable filter that is changed to the different spectral transmittances, and the spectral distributions.
 7. The device according to claim 5, wherein the image processing unit estimates the spectral reflectivity based on the plural images captured under different spectral distributions of the light sources
 8. The device according to claim 1, wherein the estimation unit includes an input processing unit configured to accept an input value according to the spectral reflectivity, and estimate the spectral reflectivity according to the input value. 